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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Assessment of physically-based and data-driven models to predict microbial water quality in open channels
Minyoung Kim1, Charles P Gerba, Christopher Y Choi
1Agricultural Safety Engineering Division, Department of Agricultural Engineering, National Academy ofAgricultural Science, Rural Development Administration, 249 Seodun-dong, Gwonson-gu, Suwon, 441-707, Korea. mykim75@korea.kr
Journal of Environmental Sciences (China)
|October 7, 2010
Summary
Both physically-based hydraulic models and artificial neural networks (ANNs) accurately simulate microorganism transport in water systems. These methods can enhance water facility vulnerability assessments and emergency planning.
Area of Science:
- Environmental Engineering
- Water Resource Management
- Microbiology
Background:
- Simulating the fate and transport of microorganisms in water systems is crucial for public health and water security.
- Traditional physically-based models and emerging data-driven approaches offer different strengths in predicting microbial contamination.
- Understanding the comparative performance of these models is essential for effective water quality management.
Purpose of the Study:
- To evaluate the efficacy of a physically-based hydraulic modeling tool and artificial neural networks (ANNs) for simulating microorganism fate and transport.
- To compare the predictive accuracy of these two distinct modeling approaches using experimental data.
- To explore the complementary utility of these models for water facility risk assessment.
Main Methods:
- Experimental setup: A controlled pipe network was used with Escherichia coli (a fecal coliform indicator) to generate reliable data.
- Physically-based model inputs: Morphological (pipe size, length, slope) and hydraulic (flow rate) data were utilized.
- ANN model inputs: Water quality parameters including conductivity, pH, and turbidity were employed.
Main Results:
- Both the physically-based model (R = 0.914-0.977) and ANNs (R = 0.949-0.980) demonstrated high accuracy in simulating microorganism transport.
- The models performed well across various conditions, with a minor exception noted at a low flow rate (q = 31.56 cm³/sec).
- The study confirmed the robust predictive capabilities of both modeling strategies for microbial contamination.
Conclusions:
- Physically-based hydraulic models and ANNs are effective tools for simulating microorganism fate and transport in water systems.
- These modeling approaches can be used together to enhance the assessment of water facility vulnerability.
- The findings support the development of proactive emergency plans for water systems based on hypothetical contamination scenarios.
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